Dynamic Relationship between Gross Domestic Product and Domestic Investment in Rwanda
Bibliographic record
Abstract
This study uses a VAR model to analyse the dynamic relationship between gross domestic product (GDP) and domestic investment (DI) in Rwanda for the period 1970 to 2011. Several selection lag criteria chose a maximum lag of one, and a bivariate VAR(1) model specification in levels was adopted. Unit root tests show that both GDP and DI series are nonstationary in levels but stationary in first differences, implying that both are integrated of order one I(1). Tests of cointegration established that GDP and DI are CI(1,1), suggesting there is a long-run equilibrium relationship between the two series. The error correction model indicates that DI adjusts to GDP with a lag whereby 0.2 percent of the discrepancy between long-term and short-term DI is corrected within the year. Granger causality tests show that there is unidirectional causality where GDP causes DI. The bivariate VAR (1) was unstable when estimated at levels, but was stable in first differences. Finally it was found out that GDP almost perfectly predicts DI in the estimated VAR (1) model. The forecasted value of DI in 2011 was 22.6% of GDP while the actual value was 22.7% of GDP. The small discrepancy may be attributed to the appropriate policy measures the Rwandan government and the private sector federation have thus far taken to facilitate investors in their businesses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".